Application Architect - AI Integration

About the role

As an Application Architect for AI Integration, this role is responsible for designing and governing end-to-end architectures that embed AI capabilities into enterprise applications and workflows. The Application Architect collaborates with stakeholders to translate business objectives into scalable designs, balancing vendor-managed services with custom development, and drives governance for AI integration.

Responsibilities

• Designing Architectures: defining reference patterns for AI services, models, and orchestration layers to integrate seamlessly with existing systems, APIs, and data platforms.
• Collaborating with Stakeholders: translating business objectives into scalable, resilient, and policy-compliant designs, ensuring performance, security, compliance, and cost objectives are met.
• Governing AI Integration: driving governance for prompts, guardrails, and lifecycle management, ensuring observability, privacy, and responsible AI principles are embedded from design through operations.

Required Technical And Professional Expertise

• Core Architecture: 12+ years of enterprise application architecture experience, with at least 3 years in a lead or principal architect role owning cross-domain target-state design
• Proven experience defining: microservices, event-driven (Kafka/SQS/EventBridge), and API-first architectures at scale — including versioning, lifecycle governance, and breaking-change management
• AWS cloud architecture: (Solutions Architect Professional or equivalent depth) — EKS, RDS/Aurora, API Gateway, Lambda, SQS/SNS, MSK, and AI/ML services (Bedrock, SageMaker)
• Data architecture experience: OLTP/OLAP separation, CDC patterns, data domain ownership, master data management, and real-time vs. batch pipeline design
• AI & Agentic Architecture: Hands-on architecture experience designing systems that embed AI capabilities — RAG pipelines, LLM orchestration (LangChain, LlamaIndex, or equivalent), and agentic workflow patterns
• Experience integrating AI/ML models into production enterprise systems: including model serving, prompt governance, output guardrails, and cost/latency management
• Familiarity with AI governance frameworks: responsible AI principles, explainability requirements, audit trails for AI-assisted decisions — particularly in regulated industries
• Experience evaluating and selecting AI services: (managed APIs vs. self-hosted models) based on data privacy, compliance, and total cost of ownership constraints
• Domain & Regulatory: Financial services or insurance domain experience — understanding of policy lifecycle, commission structures, agent licensing, or carrier integration is strongly preferred
• Experience designing systems that operate under regulatory frameworks: FINRA, FSRA, SOC 2, GLBA, CCPA, or equivalent — with audit logging, data retention, and access control as first-class architecture concerns
• Technical Leadership: Demonstrated ability to drive architecture decisions through ambiguity — producing clear documentation, facilitating design reviews, and building consensus across engineering and business stakeholders
• Experience working in Agile/scaled delivery environments: integrating architecture practice into sprint cadence without becoming a bottleneck
• Track record of mentoring senior engineers and establishing architecture standards that teams adopt and maintain

Preferred Technical And Professional Experience

• Experience with agent/broker distribution platforms, MLM commission hierarchies, or multi-carrier insurance marketplaces
• Familiarity with ACORD standards, NIPR/Sircon integrations, or DTCC financial messaging
• Experience with Valkey/Redis, Kafka, or distributed caching architecture in high-availability Kubernetes environments
• Prior experience in a consultancy or systems integrator context — ability to operate as a trusted advisor alongside a client engineering team